1. Field of the Invention
The invention relates to an image processing method, and more particularly to an image processing method for adjusting image parameters according to the local characteristic of an image.
2. Description of the Related Art
With the development of digital signal processing, the technological development of digital image processing has also flourished. The digitalization of an image is to convert it into a form which can be stored in a computer's memory or some storage device such as a hard disk or CD-ROM. Once the image has been digitalized, it can be operated upon by various image processing operations, such as image compression, image enhancement, etc.
In an image processing context, the histogram of an image normally refers to a histogram of pixel values. A histogram is a graph showing the number of pixels in an image at each different pixel values found in that image. For an 8-bit grayscale image, there are 256 different possible pixel values, and so the histogram will graphically display 256 numbers showing the distribution of pixels amongst those grayscale values. Histograms can also be taken of color images, either individual histogram of red, green and blue channels can be taken, or a 3-D histogram can be produced, with the three axes representing the red, blue and green channels, and brightness at each point representing the pixel count. The exact output from the operation depends upon the implementation, it may simply be a picture of the required histogram in a suitable image format, or it may be a data file of some sort representing the histogram statistically.
FIG. 1A illustrates a histogram 10 of an 8-bit grayscale image. As described above, there are 256 different possible pixel values and the histogram 10 statistically shows the number of pixels in an image at each different pixel values found in that image. Some characteristic of the image can be found by analyzing the histogram, such as the most distributed grayscale value of the image. It is also possible to further derive the histogram 10 into some other form of statistical function, such as a density function 15 shown in FIG. 1B. By analyzing the histogram and other statistical functions derived by the histogram, a more detailed characteristic of the image can be found, and some image processing technologies, such as quantization and thresholding (converting a grayscale image into binary) can be operated upon the image according to histogram and/or other statistical functions.